Taras Agryzkov

TL;DR: A new method to establish a ranking of nodes in an urban network, adapting the concept of PageRank vector to urban networks, and is applied to a real example, in which a part of the city of Murcia (Spain) is considered.

TL;DR: A new model of centrality for urban networks is proposed based on the concept of Eigenvector Centrality forUrban street networks which incorporates information from both topology and data residing on the nodes, and is able to measure the influence of two factors, the topology of the network and the geo-referenced data extracted from thenetwork and associated to the nodes.

TL;DR: The question of whether there is a relationship between the data provided by Foursquare regarding users’ tastes and preferences and fieldwork carried out in cities, especially those connected with business and leisure is considered.

TL;DR: A new centrality index for urban street patterns represented as networks in geographical space that combines the idea behind the random-walk betweenness centrality measure and the idea of ranking the nodes of a network produced by an adapted PageRank algorithm is proposed.

TL;DR: A centrality measure, with a practical application in urban network, has been proposed, which establishes a ranking of nodes focused on the topological data distribution and a visual comparison of the results produced by the various measures calculated from the algorithms studied.